There is an urgent need for novel anti-malaria interventions, due to the growing resistance of Plasmodium parasites to available drugs. The long-term objective of our research is to identify potential drug or vaccine targets using an efficient and cost-effective data-mining approach. This proposed study is aimed at the identification of biological networks as the first step toward a systems-level understanding of parasite biology. Compared to functional genomics, which requires a priori information about the identity or function of targets, a systems-level understanding of parasite biology will allow us to identify targets based on their key roles in networks. Specific aim 1 focuses on the harvest of data relevant to three gene families: the proteases, transporters, and transcription factors that are essential components in cellular networks for parasite growth and infection. Our project will encompass genomic data, expression data, and data derived from other high throughput experiments and bioinformatic and statistical analyses. Specific aim 2 focuses on the integration and encapsulation of these data in the form of network models. The models will be inferred using probabilistic graphical methods drawn from the field of engineering. Specific aim 3 focuses on the exploration of these models, particularly in the light of data from wet lab gene mapping and drug resistance experiments. Our "biological maps" will enable the visualization of evolutionary forces driving changes in the parasite's phenotype and should allow us to identify network design weaknesses in the parasite--potential vulnerabilities that could result in new malarial control strategies.